System and method for universal data modeling

ABSTRACT

Systems and methods for universal data modeling are disclosed. According to one embodiment, in an information processing apparatus comprising at least one computer processor, a method for universal data modeling may include: (1) acquiring a data of a plurality of different data types from a plurality of data sources, the data comprising anonymous and nonanonymous customer data for customers of an organization; (2) ingesting the data into a data repository; (3) applying at least one quality control check to the data; (4) enriching the data; and (5) performing data analytics on the data to associate some of the data with one of the plurality of customers.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 62/533,737, filed Jul. 18, 2017, the disclosure of which ishereby incorporated, by reference, in its entirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present disclosure generally relates to systems and methods foruniversal data modeling.

2. Description of the Related Art

In general, Customer Relationship Management (CRM) tools typically trackseparate distribution channels. Thus, marketing personnel cannot gain a“holistic” view of how effective a marketing campaign may be. Forexample, management may wish to know what marketing campaigns were mosteffective in reaching certain goals, how often did meetings occur aftera digital interaction, what targets went back to an organization'swebsite after a meeting or phone call, the amount of sales that isattributable to digital interactions and campaigns, what is the returnon investment (“ROI”) on a marketing investment, etc. In addition, thereis no way to know which marketing channels marketers should be used forspecific customers or small clusters of customers.

SUMMARY OF THE INVENTION

Systems and methods for universal data modeling are disclosed. Accordingto one embodiment, in an information processing apparatus comprising atleast one computer processor, a method for universal data modeling mayinclude: (1) acquiring a data of a plurality of different data typesfrom a plurality of data sources, the data comprising anonymous andnonanonymous customer data for customers of an organization; (2)ingesting the data into a data repository; (3) applying at least onequality control check to the data; (4) enriching the data; and (5)performing data analytics on the data to associate some of the data withone of the plurality of customers.

In one embodiment, the data sources may include data sources that areinternal to the organization and data sources that are external to theorganization.

In one embodiment, the data types may include paid or bought data,earned data, owned data, or unstructured data.

In one embodiment, the step of ingesting the data may include adaptingan ingestion framework in response to a change in a format for the data.This may include automatically rewriting code for the ingestionframework.

In one embodiment, the quality control check may check for a missingvalue, an incorrect format, or data integrity. In another embodiment,the quality control check may check for an anomaly.

In one embodiment, the method may further include assigning a score tothe data based on the quality control check.

In one embodiment, the step of enriching the data may include applyingnatural language processing to process the data.

In one embodiment, the step of performing data analytics on the data toassociate the data with one of the plurality of customers may includeusing machine learning to associate the data with the customer.

In one embodiment, the method may further include de-anonymizing theanonymous customer data.

In one embodiment, the step of enriching the data may include analyzingat least one customer online behavior in the data to identify a productor service with which the customer is interested; and associating theproduct or service with the customer. The customer online behavior mayinclude at least one of an interaction with a web page, a mousemovement, a cursor movement, and an activated link.

In one embodiment, the associated data may provide anorganizational-wide view of the customer's relationships with theorganization.

In one embodiment, the method may further include generating avisualization of the organizational-wide view of the customer'srelationships with the organization.

In one embodiment, the method may further include identifying a courseof action for interacting with the customer based on the associateddata.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, the objectsand advantages thereof, reference is now made to the followingdescriptions taken in connection with the accompanying drawings inwhich:

FIG. 1 depicts a system for universal data modeling according to oneembodiment;

FIG. 2 depicts a method for universal data modeling according to oneembodiment;

FIG. 3 depicts a conceptual view of a data model for a contact accordingto one embodiment;

FIG. 4 depicts a method for universal modeling according to oneembodiment; and

FIG. 5 depicts a method for processing marketing messages according toone embodiment.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Embodiments disclosed herein related to systems, methods, and devicesfor universal marketing.

Embodiments disclosed herein relate to a universal marketing tool thatmay be associated with, or tied into, a plurality of marketingplatforms, including, for example, Google AdWords and Analytics,DoubleClick Manger, DoubleClick Bid Manager, etc. to track the path(s)that customers may use to request information, to make purchases, etc.The data from the multiple sources may be normalized. By understandingwhich platform(s) customers use before making a purchase or to aid inmaking a purchase, marketers may develop highly effective marketingcampaigns.

In one embodiment, the impact of marketing on sales and model for salesleads may be identified.

In one embodiment, machine learning may be used to define a successfulcampaign, and to retarget an existing campaign.

In one embodiment, text analytics of sales processing of call notes maybe used to identify specific products that were discussed.

Although the disclosure may be made in the context of financial advisorsand financial products, it should be recognized that the disclosure isnot so limited and may have applicability to any industry wheremarketing and sales efforts may be tracked.

An example analytics process may include understanding businessopportunities and business requirements (e.g., during a period ofinterest, how often does a Financial Advisor (FA) visit the web? What isaverage time gap between web visits for FAs? What is the difference ofopen rate between marketing emails for specific bonds and core bonds?).Then, the data may be understood, prepared, modeled, evaluated,deployed, and monitored.

In embodiments, marking data may be presented in a highly usable,de-normalized, curated format. Predictive analytics models andperformance, index and bench marks, market data, economic indicators,product strategy, etc. may be automated and applied as is necessaryand/or desired.

In embodiments, the systems and methods disclosed herein may measure thebusiness value of marketing campaigns, optimize sales team time, and mayuse previously-unused business data.

In embodiments, the systems and methods may integrate, for example,records of client advisor conversations, records of phone logs, recordsof web activity, records of marketing activity, records of emailactivity, records of transactions and redemptions, etc. These recordsmay reside on different platforms; thus, in embodiments, the eachplatform may communicate the data using a universal language for eachplatform having universal structure

In embodiments, cookies or other identifiers may be used to track webvisits. For example, when a user accesses a website, the user's cookie,or cookies, may be retrieved. The system may then review records ofinformation to identify the user associated with the cookie. Thus,embodiments may de-anonymize anonymous traffic.

In one embodiment, when data is received from, for example, GoogleAnalytics, it may be in JSON format. Thus, it is nested data and it isnot structured in any useful way. Thus, embodiments use a data structuretransformation process using algorithms to de-anonymize the data. Thus,source data may be formatted in a generic representation so it has thesame format regardless of the data source.

Referring to FIG. 1, a system for universal marketing is disclosedaccording to one embodiment. System 100 may include data sources 110 ₁,110 ₂, 110 _(n). Examples of data sources 110 include internal datasources, cloud data sources, external data sources, etc.

In an financial institution environment, data may include paid/boughtdata (e.g., fund performance, consortium data, etc.); earned data (e.g.,portfolio insights, holdings at the financial advisor level, webbehavior (e.g., user actions on a web page, mouse and/or cursormovements, activated links, etc.), interest profiles, etc.), owned data(e.g., internal fund performance, transaction and holdings data, salesactivity, etc.), and unstructured data (e.g., call notes, inbound andoutbound emails and messages, etc.).

System 100 may further include data ingestion services 120, which mayingest the data from data sources 110 ₁, 110 ₂, 110 _(n). Data ingestionservices 120 may process the data from data sources 110 ₁, 110 ₂, 110_(n) into raw data 130 ₁, 130 ₂, 130 _(n) so it can be normalized,cleansed, and enriched by data normalization, cleaning, and enrichmentlayer 140. Raw data 130 ₁, 130 ₂, 130 _(n) may include raw historicaldata for reference.

In one embodiment, data ingestion services 120 may be based on adynamic, meta-data driven approach that ingests data in various formats(relational and non-relational, structured and unstructured) across fromdata from data sources 110 ₁, 110 ₂, 110 _(n).

For example, data may be cleaned using, for example, de-duplication,removing extra characters, converting the data to a consistent format,etc. Other techniques may be applied to cleanse the data as is necessaryand/or desired.

The output of data normalization, cleaning, and enrichment layer 140 isconformed data 150 ₁, 150 ₂, 150 _(n). Conformed data 150 ₁, 150 ₂, 150_(n) may include a consumable data set that may be cleansed, normalized,and enriched by, for example, data types, format, keys, etc.

Conformed data 150 ₁, 150 ₂, 150 _(n) may be provided to dataintegration layer 160, which may integrate the data to be available indata model 170. Data model 170 may perform, or may make data availablefor, data analytics, the application of business logic, integration,etc. For example, in a financial institution environment, the data maybe used for segmentation (e.g., identification of big clients), resourceallocation (e.g., what is the most return on investment efficientcoverage model), what is the next best action (e.g., what are thehottest leads), what is the next best product (e.g., what products arethey interested in), etc.

In one embodiment, in integration layer 160, data may be integratedusing, for example, fuzzy matching, text mining, etc. In one embodiment,the data may be normalized across some, or all, data sets.

In one embodiment, business logic may be applied. For example, metricscalculations may be performed, and dynamic and user-driven mappings andmetrics may be applied.

In one embodiment, the data may be validated. For example, checks, suchas data populations checks, missing value checks, and comparisons acrossthe data set may be performed. The data may then be integrated into thesingle data model.

In one embodiment, the data from data model 170 may be output or madeaccessible to interface(s) 180, which may include, for example,workstations, applications, third parties, etc. or as otherwisenecessary and/or desired.

In one embodiment, visualization models may provide informationregarding the success of campaigns. Machine learning based on web sitevisits and web data access may be used in order to identify hits basedon, for example, financial advisor contact.

In one embodiment, messages, such as summaries of event registrations,alerts, etc. may be generated as is necessary and/or desired.

Referring to FIG. 2, a method for universal modeling is disclosedaccording to one embodiment. In step 205, data may be acquired frominternal and/or external data sources. In one embodiment, internal datasources may include, for example, managed account vendor files, regionaldata warehouses, enterprise content management, real estate, iLite data(e.g., accounts, positions, transactions, instruments, foreign exchangerates, market prices, etc.), etc. Cloud data sources may provide datathat may be stored in the cloud, such as digital marking data (e.g.,Google Analytics, Google AdWords, Site Catalyst, etc.), customer data(e.g., funds data, institutional data, marketing data, etc.), and anyother data as is necessary and/or desired. External data sources mayprovide market and/or opportunity data from third parties. Examplesinclude market metrics, financial information, etc.

In an financial institution environment, data may include paid/boughtdata (e.g., fund performance, consortium data, etc.); earned data (e.g.,portfolio insights, holdings at the financial advisor level, webbehavior (e.g., user actions on a web page, mouse and/or cursormovements, activated links, etc.), interest profiles, etc.), owned data(e.g., internal fund performance, transaction and holdings data, salesactivity, etc.), and unstructured data (e.g., call notes, inbound andoutbound emails and messages, etc.).

In step 210, the data may be ingested. For example, the data from thedata sources may be automatically ingested in various formats (e.g.,relational and non-relational, structured and unstructured, etc.),across multiple data sources, etc. In one embodiment, the ingestionprocess may be flexible to adapt to changes in the source format. Forexample, when a new column is added, the new column is detected and theingestion framework may adapt to ingest the data. The process mayautomatically re-write code to adapt to a new object structure. It maydetect mappings affected by physical changes in a data source and maydynamically generate and execute all the rewritings on a data lake thatare consistent with the semantics of the changed objects in the datasource.

In addition, the ingestion process may dynamically detect the loadmethod based on volume to ensure optimal load performance. It maycapture and apply historical changes through, for example, an in-memoryhashing algorithm while loading data into the data lake. This mayimprove time-to-market, reduce delivery cycle, and provide material costsavings.

In step 215, quality control may be applied to the data. In oneembodiment, the quality control may be an automated process that isapplied before the data is used. In one embodiment, anomaly detectionand error detection may be performed, and alerts may be generated beforethe data is consumed.

In one embodiment, data quality rules may be applied at every stage ofdata processing and storage (e.g., at load time, raw zone, conformedzone, abstraction layer, etc.) The rules may perform a variety ofquality checks from simple (e.g., missing values, incorrect format, dataintegrity) to more complex use cases (e.g., anomaly detection) thoughcomplex business rules checks and thresholds. The data quality processmay also trigger alerts.

In one embodiment, each quality check may be assigned a score that mayindicate overall quality of each data domain. Using a user interface ordashboard, the data quality framework may facilitate the visualizationof critical issues, which allows for in-depth trend analysis,monitoring; and investigation of details.

In step 220, the data may be enriched. For example, Natural LanguageProcessing (“NLP”) may be used where unique identifiers do not exist tomatch unstructured text. For example, a customer may be contactedwithout using a customer identifier, and NLP may be used to recognizethe customer's unique identifier.

In step 225, data analytics may be performed. In one embodiment, datamay be generated using machine learning. For example, machine learningmay be used to identify/predict whether anonymous web behavior belongsto a current client, even without any contact details. Machine learningclassification models/algorithms may be used to analyze anonymousbehavior and create labels such as “Probable Client,” “Not likely to bea client,” etc. based on a machine learning score (e.g., support vectormachines).

The layers may provide a “single view of the client,” a 360 degree viewof all client information that informs the next best action, product andinvestment. For example, this view may provide a distinct andcentralized source of truth for client data, driven by flexible andconsistent data model to support agility and data-driven decision makingcapabilities. In one embodiment, a single view of the client may providea clean and integrated view of a client with aggregated layer,centralized rules and metrics and intuitive interface data to meetdemands for a holistic view of client data. This may reduce risk byproviding access to scrubbed and integrated data from a set ofauthoritative, trusted sources. In embodiments, applications may beprovided with one single interface to collect and analyze the data,significantly reducing development and deployment work for thetechnology team.

A conceptual view of a data model for a contact is illustrated in FIG.3.

Referring to FIG. 4, a method for universal modeling is disclosedaccording to another embodiment. In step 405, data may be ingested frominternal data sources, external data sources, etc. This step may besimilar to steps 205 and 210, above.

In step 410, the data files from the ingested data may be de-nested to arelational format.

In step 415, the data in the relational format may be loaded into adatabase, such as Greenplum.

In step 420, the data may be de-anonymized. In one embodiment, one ofmore cookies or other identifiers may be used to de-anonymize the databy linking the cookie or identifier to a user.

In step 425, the data may be joined and viewed with document productmeta-data.

In step 430, a user's web behavior may be filtered to identify interestin a product or service. For example, the user's actions on a web page,mouse and/or cursor movements, activated links, etc. may indicate thatthe user has an interest in a specific product and/or service.

Referring to FIG. 5, a method for processing marketing messages isprovided. In step 505, one row may be separated into separate rows foreach action taken (e.g., send, open clicked link to content).

In step 510, content viewed may be joined with document productmetadata. For example, customer service calls, customer calls, meetings,and transactions may be retrieved and formatted to genericrepresentations. Events, summit attendance, web casts, broad marketcalls, conference calls, and any other group meeting where one or morerepresentative is invited may be retrieved. In one embodiment, theseevents may be represented in generic format and may be joined withcontent viewed with document product meta-data.

When all action sources are pulled together, they may reside in onetable with each action being labeled by source type. There may be acommon set of attributes that cross sources and some are specific to asource.

In step 515, for all data sources, the data may be processed in order toprovide a summary for an advisor team level. Additional sources may beadded to generic interaction structure, such as customer support calls.

Other data formatting and/or manipulation may be used as necessaryand/or desired.

In step 520, a layer of business intelligence may be applied to focus onrelevant data. For example, in one embodiment, not every mouse movement,click, etc. needs to be tracked. In other words, a single mouse movementmay not be informative, but multiple movements may indicate that theuser is scrolling through a webpage, suggesting that the user is readingthe webpage.

In step 525, weightings may be applied to each user action. In oneembodiment, machine learning may be used to set the weightings. In oneembodiment, the weightings may be for a specific user, for multipleusers in a group (e.g., men, women, age groups, etc.), or for all users.For example, in one embodiment, if a user tends to inquire more a abouta product that he or she spends time reading, a greater weighting may beapplied to those actions than for click-throughs.

In step 530, a second layer of intelligence may be applied to predictwhich actions are most likely to create an outcome, such as an increasein sales, etc. For example, when a user visits a page, and downloads aproduct PDF, the probability of a sale may increase from 5 percent to 40percent. Thus, these types of transactions may be highlighted.

In one embodiment, machine learning may be used to identify suchactions. For example, based on historical data, the system may learnwhich actions are likely to result in an increase in sales, and actionsthat are less likely to result in increased sales. The machine learningmay result in weighting of different actions.

Embodiments may provide some of all of the following: a single,distributed platform to support many applications built by variety ofteams consuming client data, fully automated meta-data driven ingestionframework that instantly adapts to changes in source data model andtransforms target to land new data structure, high-throughput to handlelarge volumes of data like web activities from Google Analytics,reliable to capture and store historical data and critical updates,scalable to archive data for long periods and support integration withother systems, a user interface that allows to search and connect data,granular data entitlement, scalable micro-services for integration, etc.

Hereinafter, general aspects of implementation of the systems andmethods of the invention will be described.

The system of the invention or portions of the system of the inventionmay be in the form of a “processing machine,” such as a general purposecomputer, for example. As used herein, the term “processing machine” isto be understood to include at least one processor that uses at leastone memory. The at least one memory stores a set of instructions. Theinstructions may be either permanently or temporarily stored in thememory or memories of the processing machine. The processor executes theinstructions that are stored in the memory or memories in order toprocess data. The set of instructions may include various instructionsthat perform a particular task or tasks, such as those tasks describedabove. Such a set of instructions for performing a particular task maybe characterized as a program, software program, or simply software.

In one embodiment, the processing machine may be a specializedprocessor.

As noted above, the processing machine executes the instructions thatare stored in the memory or memories to process data. This processing ofdata may be in response to commands by a cardholder or cardholders ofthe processing machine, in response to previous processing, in responseto a request by another processing machine and/or any other input, forexample.

As noted above, the processing machine used to implement the inventionmay be a general purpose computer. However, the processing machinedescribed above may also utilize any of a wide variety of othertechnologies including a special purpose computer, a computer systemincluding, for example, a microcomputer, mini-computer or mainframe, aprogrammed microprocessor, a micro-controller, a peripheral integratedcircuit element, a CSIC (Customer Specific Integrated Circuit) or ASIC(Application Specific Integrated Circuit) or other integrated circuit, alogic circuit, a digital signal processor, a programmable logic devicesuch as a FPGA, PLD, PLA or PAL, or any other device or arrangement ofdevices that is capable of implementing the steps of the processes ofthe invention.

The processing machine used to implement the invention may utilize asuitable operating system. Thus, embodiments of the invention mayinclude a processing machine running the iOS operating system, the OS Xoperating system, the Android operating system, the Microsoft Windows™operating systems, the Unix operating system, the Linux operatingsystem, the Xenix operating system, the IBM AIX™ operating system, theHewlett-Packard UX™ operating system, the Novell Netware™ operatingsystem, the Sun Microsystems Solaris™ operating system, the OS/2™operating system, the BeOS™ operating system, the Macintosh operatingsystem, the Apache operating system, an OpenStep™ operating system oranother operating system or platform.

It is appreciated that in order to practice the method of the inventionas described above, it is not necessary that the processors and/or thememories of the processing machine be physically located in the samegeographical place. That is, each of the processors and the memoriesused by the processing machine may be located in geographically distinctlocations and connected so as to communicate in any suitable manner.Additionally, it is appreciated that each of the processor and/or thememory may be composed of different physical pieces of equipment.Accordingly, it is not necessary that the processor be one single pieceof equipment in one location and that the memory be another single pieceof equipment in another location. That is, it is contemplated that theprocessor may be two pieces of equipment in two different physicallocations. The two distinct pieces of equipment may be connected in anysuitable manner. Additionally, the memory may include two or moreportions of memory in two or more physical locations.

To explain further, processing, as described above, is performed byvarious components and various memories. However, it is appreciated thatthe processing performed by two distinct components as described abovemay, in accordance with a further embodiment of the invention, beperformed by a single component. Further, the processing performed byone distinct component as described above may be performed by twodistinct components. In a similar manner, the memory storage performedby two distinct memory portions as described above may, in accordancewith a further embodiment of the invention, be performed by a singlememory portion. Further, the memory storage performed by one distinctmemory portion as described above may be performed by two memoryportions.

Further, various technologies may be used to provide communicationbetween the various processors and/or memories, as well as to allow theprocessors and/or the memories of the invention to communicate with anyother entity; i.e., so as to obtain further instructions or to accessand use remote memory stores, for example. Such technologies used toprovide such communication might include a network, the Internet,Intranet, Extranet, LAN, an Ethernet, wireless communication via celltower or satellite, or any client server system that providescommunication, for example. Such communications technologies may use anysuitable protocol such as TCP/IP, UDP, or OSI, for example.

As described above, a set of instructions may be used in the processingof the invention. The set of instructions may be in the form of aprogram or software. The software may be in the form of system softwareor application software, for example. The software might also be in theform of a collection of separate programs, a program module within alarger program, or a portion of a program module, for example. Thesoftware used might also include modular programming in the form ofobject oriented programming. The software tells the processing machinewhat to do with the data being processed.

Further, it is appreciated that the instructions or set of instructionsused in the implementation and operation of the invention may be in asuitable form such that the processing machine may read theinstructions. For example, the instructions that form a program may bein the form of a suitable programming language, which is converted tomachine language or object code to allow the processor or processors toread the instructions. That is, written lines of programming code orsource code, in a particular programming language, are converted tomachine language using a compiler, assembler or interpreter. The machinelanguage is binary coded machine instructions that are specific to aparticular type of processing machine, i.e., to a particular type ofcomputer, for example. The computer understands the machine language.

Any suitable programming language may be used in accordance with thevarious embodiments of the invention. Illustratively, the programminglanguage used may include assembly language, Ada, APL, Basic, C, C++,COBOL, dBase, Forth, Fortran, Java, Modula-2, Pascal, Prolog, REXX,Visual Basic, and/or JavaScript, for example. Further, it is notnecessary that a single type of instruction or single programminglanguage be utilized in conjunction with the operation of the system andmethod of the invention. Rather, any number of different programminglanguages may be utilized as is necessary and/or desirable.

Also, the instructions and/or data used in the practice of the inventionmay utilize any compression or encryption technique or algorithm, as maybe desired. An encryption module might be used to encrypt data. Further,files or other data may be decrypted using a suitable decryption module,for example.

As described above, the invention may illustratively be embodied in theform of a processing machine, including a computer or computer system,for example, that includes at least one memory. It is to be appreciatedthat the set of instructions, i.e., the software for example, thatenables the computer operating system to perform the operationsdescribed above may be contained on any of a wide variety of media ormedium, as desired. Further, the data that is processed by the set ofinstructions might also be contained on any of a wide variety of mediaor medium. That is, the particular medium, i.e., the memory in theprocessing machine, utilized to hold the set of instructions and/or thedata used in the invention may take on any of a variety of physicalforms or transmissions, for example. Illustratively, the medium may bein the form of paper, paper transparencies, a compact disk, a DVD, anintegrated circuit, a hard disk, a floppy disk, an optical disk, amagnetic tape, a RAM, a ROM, a PROM, an EPROM, a wire, a cable, a fiber,a communications channel, a satellite transmission, a memory card, a SIMcard, or other remote transmission, as well as any other medium orsource of data that may be read by the processors of the invention.

Further, the memory or memories used in the processing machine thatimplements the invention may be in any of a wide variety of forms toallow the memory to hold instructions, data, or other information, as isdesired. Thus, the memory might be in the form of a database to holddata. The database might use any desired arrangement of files such as aflat file arrangement or a relational database arrangement, for example.

In the system and method of the invention, a variety of “cardholderinterfaces” may be utilized to allow a cardholder to interface with theprocessing machine or machines that are used to implement the invention.As used herein, a cardholder interface includes any hardware, software,or combination of hardware and software used by the processing machinethat allows a cardholder to interact with the processing machine. Acardholder interface may be in the form of a dialogue screen forexample. A cardholder interface may also include any of a mouse, touchscreen, keyboard, keypad, voice reader, voice recognizer, dialoguescreen, menu box, list, checkbox, toggle switch, a pushbutton or anyother device that allows a cardholder to receive information regardingthe operation of the processing machine as it processes a set ofinstructions and/or provides the processing machine with information.Accordingly, the cardholder interface is any device that providescommunication between a cardholder and a processing machine. Theinformation provided by the cardholder to the processing machine throughthe cardholder interface may be in the form of a command, a selection ofdata, or some other input, for example.

As discussed above, a cardholder interface is utilized by the processingmachine that performs a set of instructions such that the processingmachine processes data for a cardholder. The cardholder interface istypically used by the processing machine for interacting with acardholder either to convey information or receive information from thecardholder. However, it should be appreciated that in accordance withsome embodiments of the system and method of the invention, it is notnecessary that a human cardholder actually interact with a cardholderinterface used by the processing machine of the invention. Rather, it isalso contemplated that the cardholder interface of the invention mightinteract, i.e., convey and receive information, with another processingmachine, rather than a human cardholder. Accordingly, the otherprocessing machine might be characterized as a cardholder. Further, itis contemplated that a cardholder interface utilized in the system andmethod of the invention may interact partially with another processingmachine or processing machines, while also interacting partially with ahuman cardholder.

It will be readily understood by those persons skilled in the art thatthe present invention is susceptible to broad utility and application.Many embodiments and adaptations of the present invention other thanthose herein described, as well as many variations, modifications andequivalent arrangements, will be apparent from or reasonably suggestedby the present invention and foregoing description thereof, withoutdeparting from the substance or scope of the invention.

Accordingly, while the present invention has been described here indetail in relation to its exemplary embodiments, it is to be understoodthat this disclosure is only illustrative and exemplary of the presentinvention and is made to provide an enabling disclosure of theinvention. Accordingly, the foregoing disclosure is not intended to beconstrued or to limit the present invention or otherwise to exclude anyother such embodiments, adaptations, variations, modifications orequivalent arrangements.

What is claimed is:
 1. A method for universal data modeling comprising:in an information processing apparatus comprising at least one computerprocessor: acquiring a data of a plurality of different data types froma plurality of data sources, the data comprising anonymous andnonanonymous customer data for customers of an organization; ingestingthe data into a data repository; applying at least one quality controlcheck to the data; enriching the data; and performing data analytics onthe data to associate some of the data with one of the plurality ofcustomers.
 2. The method of claim 1, wherein the data sources comprisedata sources that are internal to the organization and data sources thatare external to the organization.
 3. The method of claim 1, wherein thedata types comprise paid or bought data, earned data, owned data, orunstructured data.
 4. The method of claim 1, wherein the step ofingesting the data comprises adapting an ingestion framework in responseto a change in a format for the data.
 5. The method of claim 4, whereinthe step of adapting the ingestion framework in response to a change ina format for the data comprises automatically rewriting code for theingestion framework.
 6. The method of claim 1, wherein the qualitycontrol check checks for a missing value, an incorrect format, or dataintegrity.
 7. The method of claim 1, wherein the quality control checkchecks for an anomaly.
 8. The method of claim 1, further comprising:assigning a score to the data based on the quality control check.
 9. Themethod of claim 1, wherein the step of enriching the data comprisesapplying natural language processing to process the data.
 10. The methodof claim 1, wherein the step of performing data analytics on the data toassociate the data with one of the plurality of customers comprisesusing machine learning to associate the data with the customer.
 11. Themethod of claim 1, further comprising: de-anonymizing the anonymouscustomer data.
 12. The method of claim 1, wherein the step of enrichingthe data comprises: analyzing at least one customer online behavior inthe data to identify a product or service with which the customer isinterested; and associating the product or service with the customer.13. The method of claim 12, wherein the customer online behaviorcomprises at least one of an interaction with a web page, a mousemovement, a cursor movement, and an activated link.
 14. The method ofclaim 1, wherein the associated data provides an organizational-wideview of the customer's relationships with the organization.
 15. Themethod of claim 14, further comprising: generating a visualization ofthe organizational-wide view of the customer's relationships with theorganization.
 16. The method of claim 1, further comprising: identifyinga course of action for interacting with the customer based on theassociated data.